Joint Source–Channel Coding for Broadcasting Correlated Sources

Koken, Erman; Tuncel, Ertem

doi:10.1109/tcomm.2017.2698031

Cited by 11 publications

(4 citation statements)

References 17 publications

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“…Actually, there exists distortion for probabilistic events during an information transfer process, which denotes the difference between the source and its corresponding reconstruction. Due to this fact, it is possible to compress data based on the allowable information loss in a certain extent [30][31][32]. In Shannon information theory, rate-distortion theory is investigated for lossy data compression, whose essence is mutual information minimization under the constraint of a certain distortion.…”

Section: Similarities and Differences Between Shannon Entropy And Mimmentioning

confidence: 99%

Attention to the Variation of Probabilistic Events: Information Processing with Message Importance Measure

Sheng

Liu

Fan

2019

Entropy

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Different probabilities of events attract different attention in many scenarios such as anomaly detection and security systems. To characterize the events’ importance from a probabilistic perspective, the message importance measure (MIM) is proposed as a kind of semantics analysis tool. Similar to Shannon entropy, the MIM has its special function in information representation, in which the parameter of MIM plays a vital role. Actually, the parameter dominates the properties of MIM, based on which the MIM has three work regions where this measure can be used flexibly for different goals. When the parameter is positive but not large enough, the MIM not only provides a new viewpoint for information processing but also has some similarities with Shannon entropy in the information compression and transmission. In this regard, this paper first constructs a system model with message importance measure and proposes the message importance loss to enrich the information processing strategies. Moreover, the message importance loss capacity is proposed to measure the information importance harvest in a transmission. Furthermore, the message importance distortion function is discussed to give an upper bound of information compression based on the MIM. Additionally, the bitrate transmission constrained by the message importance loss is investigated to broaden the scope for Shannon information theory.

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Section: Similarities and Differences Between Shannon Entropy And Mimmentioning

confidence: 99%

Attention to the Variation of Probabilistic Events: Information Processing with Message Importance Measure

Sheng

Liu

Fan

2019

Entropy

View full text Add to dashboard Cite

show abstract

“…It can be observed that larger |ρ s | results in a larger gap. Again we can use (20) to analytically compute the gap between the energy requirements of the two schemes for a certain range of distortion values: At ρ s = 0.99 and D < 0.34, the gap is approximately 0.744. Finally, as stated in Remark 12, the LQG scheme achieves approximately the same minimum energy as the SSCC-ρ z scheme, hence, OL is expected to outperform LQG.…”

Section: Figure 2b Depictsmentioning

confidence: 99%

“…The complete characterization of the achievable distortion pairs for this problem was given in [19], which also showed that a joint source-channel coding (JSCC) transmission scheme is optimal while separation-based schemes cannot achieve the optimal performance. JSCC for the transmission of correlated sources over GBCs with a source-channel bandwidth mismatch was recently studied in [20], where novel hybrid digital/analog coding schemes were proposed and shown to be superior to other schemes known in the literature. It should be noted that the transmission of correlated sources over GBCs is an important communications scenario, which applies to a vast number of practical applications, including broadcasting video [21,22], images [23] and physical measurements [24].…”

Section: Introductionmentioning

confidence: 99%

On the Energy-Distortion Tradeoff of Gaussian Broadcast Channels with Feedback

Murin

Kaspi

Dabora

et al. 2017

Entropy

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This work studies the relationship between the energy allocated for transmitting a pair of correlated Gaussian sources over a two-user Gaussian broadcast channel with noiseless channel output feedback (GBCF) and the resulting distortion at the receivers. Our goal is to characterize the minimum transmission energy required for broadcasting a pair of source samples, such that each source can be reconstructed at its respective receiver to within a target distortion, when the source-channel bandwidth ratio is not restricted. This minimum transmission energy is defined as the energy-distortion tradeoff (EDT). We derive a lower bound and three upper bounds on the optimal EDT. For the upper bounds, we analyze the EDT of three transmission schemes: two schemes are based on separate source-channel coding and apply encoding over multiple samples of source pairs, and the third scheme is a joint source-channel coding scheme that applies uncoded linear transmission on a single source-sample pair and is obtained by extending the Ozarow-Leung (OL) scheme. Numerical simulations show that the EDT of the OL-based scheme is close to that of the better of the two separation-based schemes, which makes the OL scheme attractive for energy-efficient, low-latency and low-complexity source transmission over GBCFs.

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“…However, there are two major differences. One difference is that most works focus on the source with binary values and use the EXIT chart [26,27] or the protograph EXIT (PEXIT) [16] for performance analysis, while [28,29] considered the case with the source of non-binary values, but the performance analysis of decoding was not provided. The other difference is that most works, such as [29], considered the joint source and channel decoding within two time slots.…”

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confidence: 99%

Performance analysis on joint channel decoding and state estimation in cyber-physical systems

Gong²,

Song³

et al. 2017

J Wireless Com Network

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We propose to use an mean square error (MSE) transfer chart to evaluate the performance of the proposed belief propagation (BP)-based channel decoding and state estimation scheme. We focus on two models to evaluate the performance of BP-based channel decoding and state estimation: the sequential model and the iterative model. The numerical results show that the MSE transfer chart can provide much insight about the performance of the proposed channel decoding and state estimation scheme.

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Joint Source–Channel Coding for Broadcasting Correlated Sources

Cited by 11 publications

References 17 publications

Attention to the Variation of Probabilistic Events: Information Processing with Message Importance Measure

Attention to the Variation of Probabilistic Events: Information Processing with Message Importance Measure

On the Energy-Distortion Tradeoff of Gaussian Broadcast Channels with Feedback

Performance analysis on joint channel decoding and state estimation in cyber-physical systems

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